A Theoretical Development and Analysis of Jumping Gene Genetic Algorithm.
ABSTRACT Recently, gene transpositions have gained their power andattentionsincomputationalevolutionaryalgorithmdesigns.In 2004, the Jumping Gene Genetic Algorithm (JGGA) was first pro- posedandtwonewgenetranspositionoperations,namely,cut-and- paste and copy-and-paste, were introduced. Although the outper- formance of JGGA has been demonstrated by some detailed statis- tical analyses based on numerical simulations, more rigorous the- oretical justification is still in vain. In this paper, a mathematical model based on schema is derived. It then provides theoretical jus- tifications on why JGGA is superiority in searching, particularly when it is applied to solve multiobjective optimization problems. The studies are also further verified by solving some optimization problems and comparisons are made between different optimiza- tion algorithms.
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ABSTRACT: A planar ultrawideband (UWB) monopole antenna is proposed in this paper. It has a planar multiple-trapezoidal monopole with adjustable dimensions. The proposed antenna is incorporated with the rectangular/rounded-corner ground plane to further improve the monopole performances such as impedance bandwidth. The geometric complexity of the antenna configuration is not easily devisable when the UWB performance criteria such as low voltage standing wave ratio (VSWR), minimal antenna size, omni-directional and uniform radiation pattern, and relatively constant gain over the ultrawide frequency band are to be simultaneously met. In this paper, all of these requirements can now be easily accomplished by making use of the recently developed jumping genes (JGs) multiobjective optimization scheme. A vivid demonstration of this approach of UWB design is to select an antenna configuration from the thoroughly investigated results and come up with the ultimate recommendation of hardware prototype fabrication. This way not only confirms both the simulated and measured results are all in good agreement but also indicates that the proposed methodology is sound for the design of UWB antenna.IEEE Transactions on Antennas and Propagation 01/2009; · 2.33 Impact Factor
Conference Proceeding: The Schema Theorem and the Misallocation of Trials in the Presence of Stochastic Effects.[show abstract] [hide abstract]
ABSTRACT: Traditional selection in genetic algorithms has relied on reproduction in proportion to observed fitness. There has been recent interest in assessing the result of proportional selection on schemata in the presence of random effects (e.g., noisy evaluation of solutions). The analysis presented here indicates, in contrast with previous literature, that the introduction of noise to the evaluation of solutions can change the expected sampling of schemata, even when the noise is zero mean. Unfortunately, this misallocation of trials can also result simply from random initialization of a population.Evolutionary Programming VII, 7th International Conference, EP98, San Diego, CA, USA, March 25-27, 1998, Proceedings; 01/1998
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ABSTRACT: Jumping Genes Genetic Algorithm (JGGA) is a newly developed algorithm that is suitable for evolutionary computing. This algorithm is characterized by a genetic operator other than crossover and mutation, which is Jumping Genes Transposition. Jumping Genes Transposition can increase the ability of Genetic Algorithm in finding extreme solutions, and increase the diversity of the solutions at the same time. In this project, the performances of JGGA will be compared to the performances of Nondominated Sorting Genetic Algorithm II (NSGA-II) in order to evaluate the performance of Jumping Genes Transposition. JGGA and NSGA-II is very similar, and their difference is only the existence of Jumping Genes Transposition in JGGA. The designs of HGA IIR filters and digital filter banks are implemented using JGGA and NSGA-II, and these GA programs are used for the performance evaluation of the two algorithms. Prof. Man K F. Assessor: Dr. Tang K S